{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,15]],"date-time":"2026-06-15T03:50:25Z","timestamp":1781495425397,"version":"3.54.1"},"reference-count":34,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T00:00:00Z","timestamp":1650672000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"funder":[{"DOI":"10.13039\/501100003246","name":"Nederlandse Organisatie voor Wetenschappelijk Onderzoek","doi-asserted-by":"publisher","award":["VI.Vidi.195.152"],"award-info":[{"award-number":["VI.Vidi.195.152"]}],"id":[{"id":"10.13039\/501100003246","id-type":"DOI","asserted-by":"publisher"}]},{"name":"European Digital Media Observatory","award":["2020-EU-IA0252"],"award-info":[{"award-number":["2020-EU-IA0252"]}]},{"DOI":"10.13039\/501100004837","name":"Spanish Ministry of Science and Innovation","doi-asserted-by":"crossref","award":["PLEC2021-007681"],"award-info":[{"award-number":["PLEC2021-007681"]}],"id":[{"id":"10.13039\/501100004837","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Information Science"],"published-print":{"date-parts":[[2024,4]]},"abstract":"<jats:p>Fake news is a threat for the society and can create a lot of confusion to people regarding what is true and what not. Fake news usually contain manipulated content, such as text or images that attract the interest of the readers with the aim to convince them on their truthfulness. In this article, we propose SceneFND (Scene Fake News Detection), a system that combines textual, contextual scene and visual representation to address the problem of multimodal fake news detection. The textual representation is based on word embeddings that are passed into a bidirectional long short-term memory network. Both the contextual scene and the visual representations are based on the images contained in the news post. The place, weather and season scenes are extracted from the image. Our statistical analysis on the scenes showed that there are statistically significant differences regarding their frequency in fake and real news. In addition, our experimental results on two real world datasets show that the integration of the contextual scenes is effective for fake news detection. In particular, SceneFND improved the performance of the textual baseline by 3.48% in PolitiFact and by 3.32% in GossipCop datasets. Finally, we show the suitability of the scene information for the task and present some examples to explain its effectiveness in capturing the relevance between images and text.<\/jats:p>","DOI":"10.1177\/01655515221087683","type":"journal-article","created":{"date-parts":[[2022,4,23]],"date-time":"2022-04-23T07:37:32Z","timestamp":1650699452000},"page":"355-367","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":29,"title":["SceneFND: Multimodal fake news detection by modelling scene context information"],"prefix":"10.1177","volume":"50","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1568-2492","authenticated-orcid":false,"given":"Guobiao","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Information Management, Wuhan University, China; Department of Computer Systems and Computation, Universitat Polit\u00e8cnica de Val\u00e8ncia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Anastasia","family":"Giachanou","sequence":"additional","affiliation":[{"name":"Department of Methodology and Statistics, Utrecht University, The Netherlands"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Paolo","family":"Rosso","sequence":"additional","affiliation":[{"name":"Department of Computer Systems and Computation, Universitat Polit\u00e8cnica de Val\u00e8ncia, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"179","published-online":{"date-parts":[[2022,4,23]]},"reference":[{"key":"bibr1-01655515221087683","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijinfomgt.2019.04.004"},{"key":"bibr2-01655515221087683","doi-asserted-by":"publisher","DOI":"10.2196\/publichealth.7157"},{"key":"bibr3-01655515221087683","doi-asserted-by":"publisher","DOI":"10.3390\/fi13020029"},{"key":"bibr4-01655515221087683","doi-asserted-by":"publisher","DOI":"10.1145\/1963405.1963500"},{"key":"bibr5-01655515221087683","first-page":"430","volume-title":"Proceedings of the 2018 IEEE conference on multimedia information processing and retrieval (MIPR\u201918)","author":"Shu K"},{"key":"bibr6-01655515221087683","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-51310-8_17"},{"key":"bibr7-01655515221087683","doi-asserted-by":"publisher","DOI":"10.1016\/j.datak.2021.101960"},{"key":"bibr8-01655515221087683","first-page":"22","volume-title":"Proceedings of the 2018 conference on empirical methods in natural language processing (EMNLP\u201918)","author":"Popat K"},{"key":"bibr9-01655515221087683","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-021-06450-4"},{"key":"bibr10-01655515221087683","first-page":"849","volume-title":"Proceedings of the 24th ACM SIGKDD international conference on knowledge discovery and data mining (KDD\u201918)","author":"Wang Y"},{"key":"bibr11-01655515221087683","doi-asserted-by":"publisher","DOI":"10.1145\/3308558.3313552"},{"key":"bibr12-01655515221087683","first-page":"647","volume-title":"Proceedings of the 2020 IEEE 7th international conference on data science and advanced analytics (DSAA)","author":"Giachanou A"},{"key":"bibr13-01655515221087683","volume-title":"The image and the eye: further studies in the psychology of pictorial representation","author":"Gombrich EH","year":"1982"},{"key":"bibr14-01655515221087683","unstructured":"Ruffo G, Semeraro A, Giachanou A et al. 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